Male-biased aganglionic megacolon in the TashT mouse model of Hirschsprung disease involves upregulation of p53 protein activity and Ddx3y gene expression
Bibliographic record
Abstract
Hirschsprung disease (HSCR) is a complex genetic disorder of neural crest development resulting in incomplete formation of the enteric nervous system (ENS). This life-threatening neurocristopathy affects 1/5000 live births, with a currently unexplained male-biased ratio. To address this lack of knowledge, we took advantage of the TashT mutant mouse line, which is the only HSCR model to display a robust male bias. Our prior work revealed that the TashT insertional mutation perturbs a Chr.10 silencer-enriched non-coding region, leading to transcriptional dysregulation of hundreds of genes in neural crest-derived ENS progenitors of both sexes. Here, through sex-stratified transcriptome analyses and targeted overexpression in ENS progenitors, we show that male-biased ENS malformation in TashT embryos is not due to upregulation of Sry-the murine ortholog of a candidate gene for the HSCR male bias in humans-but instead involves upregulation of another Y-linked gene, Ddx3y. This discovery might be clinically relevant since we further found that the DDX3Y protein is also expressed in the ENS of a subset of male HSCR patients. Mechanistically, other data including chromosome conformation captured-based assays and CRISPR/Cas9-mediated deletions suggest that Ddx3y upregulation in male TashT ENS progenitors is due to increased transactivation by p53, which appears especially active in these cells yet without triggering apoptosis. Accordingly, in utero treatment of TashT embryos with the p53 inhibitor pifithrin-α decreased Ddx3y expression and abolished the otherwise more severe ENS defect in TashT males. Our data thus highlight novel pathogenic roles for p53 and DDX3Y during ENS formation in mice, a finding that might help to explain the intriguing male bias of HSCR in humans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".